Overview

Dataset statistics

Number of variables25
Number of observations2334
Missing cells1030
Missing cells (%)1.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory408.1 KiB
Average record size in memory179.1 B

Variable types

Categorical9
Text1
Numeric12
Boolean3

Alerts

brand_name is highly overall correlated with has_ir_blaster and 2 other fieldsHigh correlation
extended_memory_available is highly overall correlated with extended_upto and 4 other fieldsHigh correlation
extended_upto is highly overall correlated with extended_memory_availableHigh correlation
fast_charging is highly overall correlated with extended_memory_available and 7 other fieldsHigh correlation
fast_charging_available is highly overall correlated with fast_charging and 3 other fieldsHigh correlation
has_5g is highly overall correlated with internal_memory and 7 other fieldsHigh correlation
has_ir_blaster is highly overall correlated with brand_nameHigh correlation
has_nfc is highly overall correlated with extended_memory_available and 7 other fieldsHigh correlation
internal_memory is highly overall correlated with fast_charging and 9 other fieldsHigh correlation
num_cores is highly overall correlated with os and 1 other fieldsHigh correlation
os is highly overall correlated with brand_name and 2 other fieldsHigh correlation
price is highly overall correlated with extended_memory_available and 8 other fieldsHigh correlation
primary_camera_front is highly overall correlated with fast_charging and 6 other fieldsHigh correlation
primary_camera_rear is highly overall correlated with fast_charging_available and 4 other fieldsHigh correlation
processor_brand is highly overall correlated with brand_name and 3 other fieldsHigh correlation
processor_speed is highly overall correlated with extended_memory_available and 8 other fieldsHigh correlation
ram_capacity is highly overall correlated with fast_charging and 10 other fieldsHigh correlation
rating is highly overall correlated with fast_charging and 8 other fieldsHigh correlation
refresh_rate is highly overall correlated with has_5g and 2 other fieldsHigh correlation
resolution is highly overall correlated with fast_charging_available and 2 other fieldsHigh correlation
screen_size is highly overall correlated with internal_memory and 2 other fieldsHigh correlation
num_front_cameras is highly imbalanced (86.7%)Imbalance
os is highly imbalanced (70.7%)Imbalance
rating has 265 (11.4%) missing valuesMissing
processor_brand has 83 (3.6%) missing valuesMissing
processor_speed has 151 (6.5%) missing valuesMissing
fast_charging has 504 (21.6%) missing valuesMissing
extended_memory_available is uniformly distributedUniform
extended_upto has 1167 (50.0%) zerosZeros

Reproduction

Analysis started2024-02-14 02:07:07.307027
Analysis finished2024-02-14 02:07:29.693547
Duration22.39 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

brand_name
Categorical

HIGH CORRELATION 

Distinct32
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
xiaomi
270 
vivo
263 
samsung
253 
realme
252 
oppo
153 
Other values (27)
1143 

Length

Max length10
Median length8
Mean length5.5556984
Min length2

Characters and Unicode

Total characters12967
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowoneplus
2nd rowtecno
3rd rowxiaomi
4th rowrealme
5th rowxiaomi

Common Values

ValueCountFrequency (%)
xiaomi 270
11.6%
vivo 263
11.3%
samsung 253
10.8%
realme 252
10.8%
oppo 153
 
6.6%
motorola 151
 
6.5%
tecno 100
 
4.3%
infinix 91
 
3.9%
poco 90
 
3.9%
honor 88
 
3.8%
Other values (22) 623
26.7%

Length

2024-02-14T07:37:29.824431image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
xiaomi 270
11.6%
vivo 263
11.3%
samsung 253
10.8%
realme 252
10.8%
oppo 153
 
6.6%
motorola 151
 
6.5%
tecno 100
 
4.3%
infinix 91
 
3.9%
poco 90
 
3.9%
honor 88
 
3.8%
Other values (22) 623
26.7%

Most occurring characters

ValueCountFrequency (%)
o 2181
16.8%
i 1400
10.8%
a 1295
10.0%
m 955
 
7.4%
e 922
 
7.1%
n 870
 
6.7%
l 731
 
5.6%
s 658
 
5.1%
p 607
 
4.7%
v 591
 
4.6%
Other values (15) 2757
21.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12967
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 2181
16.8%
i 1400
10.8%
a 1295
10.0%
m 955
 
7.4%
e 922
 
7.1%
n 870
 
6.7%
l 731
 
5.6%
s 658
 
5.1%
p 607
 
4.7%
v 591
 
4.6%
Other values (15) 2757
21.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 12967
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 2181
16.8%
i 1400
10.8%
a 1295
10.0%
m 955
 
7.4%
e 922
 
7.1%
n 870
 
6.7%
l 731
 
5.6%
s 658
 
5.1%
p 607
 
4.7%
v 591
 
4.6%
Other values (15) 2757
21.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12967
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 2181
16.8%
i 1400
10.8%
a 1295
10.0%
m 955
 
7.4%
e 922
 
7.1%
n 870
 
6.7%
l 731
 
5.6%
s 658
 
5.1%
p 607
 
4.7%
v 591
 
4.6%
Other values (15) 2757
21.3%

model
Text

Distinct2333
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
2024-02-14T07:37:30.106949image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Length

Max length52
Median length41
Mean length19.533847
Min length6

Characters and Unicode

Total characters45592
Distinct characters68
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2332 ?
Unique (%)99.9%

Sample

1st rowOnePlus V Flip
2nd rowTecno Camon 20 Pro 4G
3rd rowXiaomi Redmi Note 12 Pro (8GB RAM + 256GB)
4th rowRealme 8 5G
5th rowXiaomi Redmi Note 10 JE 5G
ValueCountFrequency (%)
5g 622
 
6.3%
ram 456
 
4.6%
441
 
4.5%
pro 422
 
4.3%
xiaomi 266
 
2.7%
vivo 263
 
2.7%
samsung 253
 
2.6%
realme 252
 
2.6%
galaxy 248
 
2.5%
128gb 219
 
2.2%
Other values (989) 6411
65.1%
2024-02-14T07:37:30.602346image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7519
 
16.5%
o 3059
 
6.7%
a 2154
 
4.7%
G 2103
 
4.6%
i 2058
 
4.5%
e 1872
 
4.1%
l 1240
 
2.7%
2 1190
 
2.6%
1 1185
 
2.6%
P 1176
 
2.6%
Other values (58) 22036
48.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18852
41.3%
Uppercase Letter 11216
24.6%
Space Separator 7522
 
16.5%
Decimal Number 6512
 
14.3%
Close Punctuation 512
 
1.1%
Open Punctuation 512
 
1.1%
Math Symbol 456
 
1.0%
Other Punctuation 8
 
< 0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 2103
18.8%
P 1176
10.5%
R 1032
9.2%
B 978
8.7%
M 959
8.6%
A 793
 
7.1%
O 548
 
4.9%
S 509
 
4.5%
X 466
 
4.2%
N 453
 
4.0%
Other values (16) 2199
19.6%
Lowercase Letter
ValueCountFrequency (%)
o 3059
16.2%
a 2154
11.4%
i 2058
10.9%
e 1872
9.9%
l 1240
 
6.6%
n 1121
 
5.9%
m 1086
 
5.8%
r 1035
 
5.5%
t 749
 
4.0%
s 657
 
3.5%
Other values (15) 3821
20.3%
Decimal Number
ValueCountFrequency (%)
2 1190
18.3%
1 1185
18.2%
5 1138
17.5%
0 633
9.7%
6 517
7.9%
8 510
7.8%
4 495
7.6%
3 464
 
7.1%
7 223
 
3.4%
9 157
 
2.4%
Space Separator
ValueCountFrequency (%)
7519
> 99.9%
  3
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 512
100.0%
Open Punctuation
ValueCountFrequency (%)
( 512
100.0%
Math Symbol
ValueCountFrequency (%)
+ 456
100.0%
Other Punctuation
ValueCountFrequency (%)
. 8
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 30068
66.0%
Common 15524
34.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 3059
 
10.2%
a 2154
 
7.2%
G 2103
 
7.0%
i 2058
 
6.8%
e 1872
 
6.2%
l 1240
 
4.1%
P 1176
 
3.9%
n 1121
 
3.7%
m 1086
 
3.6%
r 1035
 
3.4%
Other values (41) 13164
43.8%
Common
ValueCountFrequency (%)
7519
48.4%
2 1190
 
7.7%
1 1185
 
7.6%
5 1138
 
7.3%
0 633
 
4.1%
6 517
 
3.3%
) 512
 
3.3%
( 512
 
3.3%
8 510
 
3.3%
4 495
 
3.2%
Other values (7) 1313
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45589
> 99.9%
Punctuation 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7519
 
16.5%
o 3059
 
6.7%
a 2154
 
4.7%
G 2103
 
4.6%
i 2058
 
4.5%
e 1872
 
4.1%
l 1240
 
2.7%
2 1190
 
2.6%
1 1185
 
2.6%
P 1176
 
2.6%
Other values (57) 22033
48.3%
Punctuation
ValueCountFrequency (%)
  3
100.0%

price
Real number (ℝ)

HIGH CORRELATION 

Distinct660
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30996.813
Minimum3499
Maximum199990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2024-02-14T07:37:30.780562image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum3499
5-th percentile6995.2
Q111919
median19990
Q337999
95-th percentile98993.15
Maximum199990
Range196491
Interquartile range (IQR)26080

Descriptive statistics

Standard deviation30628.405
Coefficient of variation (CV)0.98811466
Kurtosis6.1428936
Mean30996.813
Median Absolute Deviation (MAD)10000
Skewness2.30656
Sum72346561
Variance9.3809919 × 108
MonotonicityNot monotonic
2024-02-14T07:37:30.951979image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29990 40
 
1.7%
19990 35
 
1.5%
14990 35
 
1.5%
9990 34
 
1.5%
14999 33
 
1.4%
15990 32
 
1.4%
12999 32
 
1.4%
24999 31
 
1.3%
8999 31
 
1.3%
24990 31
 
1.3%
Other values (650) 2000
85.7%
ValueCountFrequency (%)
3499 2
0.1%
3999 2
0.1%
4499 2
0.1%
4688 1
< 0.1%
4789 1
< 0.1%
4799 2
0.1%
4899 1
< 0.1%
4990 1
< 0.1%
4999 1
< 0.1%
5299 2
0.1%
ValueCountFrequency (%)
199990 1
< 0.1%
197999 1
< 0.1%
196900 1
< 0.1%
191999 1
< 0.1%
187990 1
< 0.1%
184999 1
< 0.1%
179990 2
0.1%
179900 2
0.1%
177990 1
< 0.1%
169999 2
0.1%

rating
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)1.4%
Missing265
Missing (%)11.4%
Infinite0
Infinite (%)0.0%
Mean77.883519
Minimum60
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2024-02-14T07:37:31.109972image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile63
Q173
median79
Q384
95-th percentile89
Maximum89
Range29
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.8415439
Coefficient of variation (CV)0.10068297
Kurtosis-0.6548397
Mean77.883519
Median Absolute Deviation (MAD)6
Skewness-0.54993206
Sum161141
Variance61.48981
MonotonicityNot monotonic
2024-02-14T07:37:31.257520image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
84 133
 
5.7%
75 133
 
5.7%
85 126
 
5.4%
82 108
 
4.6%
86 107
 
4.6%
89 106
 
4.5%
83 105
 
4.5%
80 90
 
3.9%
79 90
 
3.9%
88 83
 
3.6%
Other values (20) 988
42.3%
(Missing) 265
 
11.4%
ValueCountFrequency (%)
60 23
1.0%
61 49
2.1%
62 30
1.3%
63 32
1.4%
64 31
1.3%
65 39
1.7%
66 37
1.6%
67 29
1.2%
68 41
1.8%
69 38
1.6%
ValueCountFrequency (%)
89 106
4.5%
88 83
3.6%
87 72
3.1%
86 107
4.6%
85 126
5.4%
84 133
5.7%
83 105
4.5%
82 108
4.6%
81 81
3.5%
80 90
3.9%

has_5g
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
True
1313 
False
1021 
ValueCountFrequency (%)
True 1313
56.3%
False 1021
43.7%
2024-02-14T07:37:31.399030image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

has_nfc
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
False
1367 
True
967 
ValueCountFrequency (%)
False 1367
58.6%
True 967
41.4%
2024-02-14T07:37:31.493541image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

has_ir_blaster
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
False
1913 
True
421 
ValueCountFrequency (%)
False 1913
82.0%
True 421
 
18.0%
2024-02-14T07:37:31.593875image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

processor_brand
Categorical

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)0.6%
Missing83
Missing (%)3.6%
Memory size18.4 KiB
snapdragon
985 
helio
464 
dimensity
454 
exynos
101 
unisoc
 
81
Other values (9)
166 

Length

Max length10
Median length9
Mean length8.142159
Min length3

Characters and Unicode

Total characters18328
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowdimensity
2nd rowhelio
3rd rowdimensity
4th rowdimensity
5th rowsnapdragon

Common Values

ValueCountFrequency (%)
snapdragon 985
42.2%
helio 464
19.9%
dimensity 454
19.5%
exynos 101
 
4.3%
unisoc 81
 
3.5%
bionic 65
 
2.8%
tiger 41
 
1.8%
kirin 22
 
0.9%
google 17
 
0.7%
spreadtrum 13
 
0.6%
Other values (4) 8
 
0.3%
(Missing) 83
 
3.6%

Length

2024-02-14T07:37:31.734803image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
snapdragon 985
43.8%
helio 464
20.6%
dimensity 454
20.2%
exynos 101
 
4.5%
unisoc 81
 
3.6%
bionic 65
 
2.9%
tiger 41
 
1.8%
kirin 22
 
1.0%
google 17
 
0.8%
spreadtrum 13
 
0.6%
Other values (4) 8
 
0.4%

Most occurring characters

ValueCountFrequency (%)
n 2698
14.7%
a 1985
10.8%
o 1731
9.4%
i 1673
9.1%
s 1637
8.9%
d 1452
7.9%
e 1094
 
6.0%
r 1074
 
5.9%
g 1060
 
5.8%
p 998
 
5.4%
Other values (17) 2926
16.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18320
> 99.9%
Decimal Number 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2698
14.7%
a 1985
10.8%
o 1731
9.4%
i 1673
9.1%
s 1637
8.9%
d 1452
7.9%
e 1094
 
6.0%
r 1074
 
5.9%
g 1060
 
5.8%
p 998
 
5.4%
Other values (13) 2918
15.9%
Decimal Number
ValueCountFrequency (%)
9 2
25.0%
8 2
25.0%
6 2
25.0%
3 2
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18320
> 99.9%
Common 8
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2698
14.7%
a 1985
10.8%
o 1731
9.4%
i 1673
9.1%
s 1637
8.9%
d 1452
7.9%
e 1094
 
6.0%
r 1074
 
5.9%
g 1060
 
5.8%
p 998
 
5.4%
Other values (13) 2918
15.9%
Common
ValueCountFrequency (%)
9 2
25.0%
8 2
25.0%
6 2
25.0%
3 2
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2698
14.7%
a 1985
10.8%
o 1731
9.4%
i 1673
9.1%
s 1637
8.9%
d 1452
7.9%
e 1094
 
6.0%
r 1074
 
5.9%
g 1060
 
5.8%
p 998
 
5.4%
Other values (17) 2926
16.0%

num_cores
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.3%
Missing9
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean7.7819355
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2024-02-14T07:37:31.869799image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q18
median8
Q38
95-th percentile8
Maximum10
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.89520651
Coefficient of variation (CV)0.11503649
Kurtosis13.986178
Mean7.7819355
Median Absolute Deviation (MAD)0
Skewness-3.7653745
Sum18093
Variance0.8013947
MonotonicityNot monotonic
2024-02-14T07:37:32.000399image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8 2154
92.3%
4 98
 
4.2%
6 56
 
2.4%
10 8
 
0.3%
9 5
 
0.2%
2 4
 
0.2%
(Missing) 9
 
0.4%
ValueCountFrequency (%)
2 4
 
0.2%
4 98
 
4.2%
6 56
 
2.4%
8 2154
92.3%
9 5
 
0.2%
10 8
 
0.3%
ValueCountFrequency (%)
10 8
 
0.3%
9 5
 
0.2%
8 2154
92.3%
6 56
 
2.4%
4 98
 
4.2%
2 4
 
0.2%

processor_speed
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct53
Distinct (%)2.4%
Missing151
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean2.4242831
Minimum1.1
Maximum3.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2024-02-14T07:37:32.170893image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile1.6
Q12
median2.3
Q32.845
95-th percentile3.2
Maximum3.78
Range2.68
Interquartile range (IQR)0.845

Descriptive statistics

Standard deviation0.50493443
Coefficient of variation (CV)0.20828196
Kurtosis-0.64471132
Mean2.4242831
Median Absolute Deviation (MAD)0.3
Skewness0.23879023
Sum5292.21
Variance0.25495878
MonotonicityNot monotonic
2024-02-14T07:37:32.383110image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2 377
16.2%
2 355
15.2%
2.4 251
10.8%
3.2 234
10.0%
2.3 154
 
6.6%
3 90
 
3.9%
1.6 71
 
3.0%
1.8 65
 
2.8%
2.84 61
 
2.6%
2.6 48
 
2.1%
Other values (43) 477
20.4%
(Missing) 151
 
6.5%
ValueCountFrequency (%)
1.1 2
 
0.1%
1.2 5
 
0.2%
1.3 24
 
1.0%
1.4 18
 
0.8%
1.5 12
 
0.5%
1.6 71
3.0%
1.8 65
2.8%
1.82 15
 
0.6%
1.84 1
 
< 0.1%
1.95 1
 
< 0.1%
ValueCountFrequency (%)
3.78 7
 
0.3%
3.46 7
 
0.3%
3.36 9
 
0.4%
3.35 8
 
0.3%
3.3 41
 
1.8%
3.25 10
 
0.4%
3.22 16
 
0.7%
3.2 234
10.0%
3.19 1
 
< 0.1%
3.13 4
 
0.2%

ram_capacity
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9001714
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2024-02-14T07:37:32.532459image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median8
Q38
95-th percentile12
Maximum24
Range23
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1432263
Coefficient of variation (CV)0.45552872
Kurtosis0.89625907
Mean6.9001714
Median Absolute Deviation (MAD)2
Skewness0.76264075
Sum16105
Variance9.8798714
MonotonicityNot monotonic
2024-02-14T07:37:32.660298image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
8 818
35.0%
4 502
21.5%
6 417
17.9%
12 300
 
12.9%
3 136
 
5.8%
2 93
 
4.0%
16 45
 
1.9%
1 17
 
0.7%
18 4
 
0.2%
24 2
 
0.1%
ValueCountFrequency (%)
1 17
 
0.7%
2 93
 
4.0%
3 136
 
5.8%
4 502
21.5%
6 417
17.9%
8 818
35.0%
12 300
 
12.9%
16 45
 
1.9%
18 4
 
0.2%
24 2
 
0.1%
ValueCountFrequency (%)
24 2
 
0.1%
18 4
 
0.2%
16 45
 
1.9%
12 300
 
12.9%
8 818
35.0%
6 417
17.9%
4 502
21.5%
3 136
 
5.8%
2 93
 
4.0%
1 17
 
0.7%

internal_memory
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.83119
Minimum8
Maximum1024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2024-02-14T07:37:32.784266image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile32
Q164
median128
Q3256
95-th percentile256
Maximum1024
Range1016
Interquartile range (IQR)192

Descriptive statistics

Standard deviation114.65595
Coefficient of variation (CV)0.74533614
Kurtosis14.829767
Mean153.83119
Median Absolute Deviation (MAD)64
Skewness2.8616796
Sum359042
Variance13145.986
MonotonicityNot monotonic
2024-02-14T07:37:32.908938image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
128 1084
46.4%
256 534
22.9%
64 414
 
17.7%
32 175
 
7.5%
512 79
 
3.4%
16 31
 
1.3%
1024 10
 
0.4%
8 6
 
0.3%
258 1
 
< 0.1%
ValueCountFrequency (%)
8 6
 
0.3%
16 31
 
1.3%
32 175
 
7.5%
64 414
 
17.7%
128 1084
46.4%
256 534
22.9%
258 1
 
< 0.1%
512 79
 
3.4%
1024 10
 
0.4%
ValueCountFrequency (%)
1024 10
 
0.4%
512 79
 
3.4%
258 1
 
< 0.1%
256 534
22.9%
128 1084
46.4%
64 414
 
17.7%
32 175
 
7.5%
16 31
 
1.3%
8 6
 
0.3%

battery_capacity
Real number (ℝ)

Distinct136
Distinct (%)5.9%
Missing12
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean4804.8523
Minimum1600
Maximum7100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2024-02-14T07:37:33.050638image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum1600
5-th percentile3500
Q14500
median5000
Q35000
95-th percentile6000
Maximum7100
Range5500
Interquartile range (IQR)500

Descriptive statistics

Standard deviation655.4226
Coefficient of variation (CV)0.13640848
Kurtosis3.4490057
Mean4804.8523
Median Absolute Deviation (MAD)0
Skewness-0.93166388
Sum11156867
Variance429578.78
MonotonicityNot monotonic
2024-02-14T07:37:33.214463image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 1217
52.1%
4500 188
 
8.1%
6000 160
 
6.9%
4000 108
 
4.6%
4600 51
 
2.2%
4800 50
 
2.1%
4700 37
 
1.6%
4300 32
 
1.4%
3000 32
 
1.4%
4400 27
 
1.2%
Other values (126) 420
 
18.0%
ValueCountFrequency (%)
1600 1
< 0.1%
1715 1
< 0.1%
1821 2
0.1%
1900 2
0.1%
2000 1
< 0.1%
2050 1
< 0.1%
2100 1
< 0.1%
2110 1
< 0.1%
2200 2
0.1%
2230 1
< 0.1%
ValueCountFrequency (%)
7100 1
 
< 0.1%
7000 11
 
0.5%
6700 1
 
< 0.1%
6500 1
 
< 0.1%
6000 160
6.9%
5800 6
 
0.3%
5600 2
 
0.1%
5580 1
 
< 0.1%
5500 24
 
1.0%
5450 2
 
0.1%

fast_charging_available
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
1
1974 
0
360 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2334
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1974
84.6%
0 360
 
15.4%

Length

2024-02-14T07:37:33.364861image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-14T07:37:33.470070image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1 1974
84.6%
0 360
 
15.4%

Most occurring characters

ValueCountFrequency (%)
1 1974
84.6%
0 360
 
15.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2334
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1974
84.6%
0 360
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2334
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1974
84.6%
0 360
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2334
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1974
84.6%
0 360
 
15.4%

fast_charging
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)2.2%
Missing504
Missing (%)21.6%
Infinite0
Infinite (%)0.0%
Mean49.448087
Minimum8
Maximum260
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2024-02-14T07:37:33.594320image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile10
Q118
median33
Q367
95-th percentile120
Maximum260
Range252
Interquartile range (IQR)49

Descriptive statistics

Standard deviation37.450332
Coefficient of variation (CV)0.75736665
Kurtosis4.1092756
Mean49.448087
Median Absolute Deviation (MAD)15
Skewness1.7060932
Sum90490
Variance1402.5274
MonotonicityNot monotonic
2024-02-14T07:37:33.754924image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
18 287
12.3%
33 276
11.8%
67 151
 
6.5%
25 122
 
5.2%
80 117
 
5.0%
66 95
 
4.1%
10 94
 
4.0%
120 93
 
4.0%
15 82
 
3.5%
44 76
 
3.3%
Other values (30) 437
18.7%
(Missing) 504
21.6%
ValueCountFrequency (%)
8 1
 
< 0.1%
10 94
 
4.0%
15 82
 
3.5%
18 287
12.3%
19 1
 
< 0.1%
20 21
 
0.9%
21 4
 
0.2%
22 22
 
0.9%
25 122
5.2%
27 1
 
< 0.1%
ValueCountFrequency (%)
260 1
 
< 0.1%
250 2
 
0.1%
240 5
 
0.2%
210 2
 
0.1%
200 8
 
0.3%
180 3
 
0.1%
165 4
 
0.2%
150 27
1.2%
135 1
 
< 0.1%
125 9
 
0.4%

num_rear_cameras
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
3
1195 
2
697 
4
267 
1
175 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2334
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 1195
51.2%
2 697
29.9%
4 267
 
11.4%
1 175
 
7.5%

Length

2024-02-14T07:37:33.895642image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-14T07:37:34.007318image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
3 1195
51.2%
2 697
29.9%
4 267
 
11.4%
1 175
 
7.5%

Most occurring characters

ValueCountFrequency (%)
3 1195
51.2%
2 697
29.9%
4 267
 
11.4%
1 175
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2334
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1195
51.2%
2 697
29.9%
4 267
 
11.4%
1 175
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2334
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1195
51.2%
2 697
29.9%
4 267
 
11.4%
1 175
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2334
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1195
51.2%
2 697
29.9%
4 267
 
11.4%
1 175
 
7.5%

num_front_cameras
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
1
2239 
2
 
87
0
 
6
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2334
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2239
95.9%
2 87
 
3.7%
0 6
 
0.3%
3 2
 
0.1%

Length

2024-02-14T07:37:34.141896image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-14T07:37:34.278366image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1 2239
95.9%
2 87
 
3.7%
0 6
 
0.3%
3 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 2239
95.9%
2 87
 
3.7%
0 6
 
0.3%
3 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2334
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2239
95.9%
2 87
 
3.7%
0 6
 
0.3%
3 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 2334
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2239
95.9%
2 87
 
3.7%
0 6
 
0.3%
3 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2334
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2239
95.9%
2 87
 
3.7%
0 6
 
0.3%
3 2
 
0.1%

screen_size
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5709683
Minimum4
Maximum8.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2024-02-14T07:37:34.451847image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6.0965
Q16.5
median6.6
Q36.7
95-th percentile6.82
Maximum8.3
Range4.3
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.36802703
Coefficient of variation (CV)0.056008037
Kurtosis10.709186
Mean6.5709683
Median Absolute Deviation (MAD)0.1
Skewness-1.2945149
Sum15336.64
Variance0.1354439
MonotonicityNot monotonic
2024-02-14T07:37:34.614556image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.5 254
 
10.9%
6.67 218
 
9.3%
6.7 214
 
9.2%
6.78 172
 
7.4%
6.6 167
 
7.2%
6.56 94
 
4.0%
6.8 83
 
3.6%
6.52 75
 
3.2%
6.58 64
 
2.7%
6.43 60
 
2.6%
Other values (86) 933
40.0%
ValueCountFrequency (%)
4 1
 
< 0.1%
4.5 5
 
0.2%
4.7 8
 
0.3%
5 17
0.7%
5.2 5
 
0.2%
5.3 2
 
0.1%
5.4 4
 
0.2%
5.42 1
 
< 0.1%
5.45 12
0.5%
5.5 22
0.9%
ValueCountFrequency (%)
8.3 1
 
< 0.1%
8.2 4
0.2%
8.03 5
0.2%
8.02 2
 
0.1%
8.01 1
 
< 0.1%
8 4
0.2%
7.92 4
0.2%
7.9 3
0.1%
7.85 4
0.2%
7.82 2
 
0.1%

resolution
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
FHD
1253 
HD
620 
QHD
395 
HD+
 
57
FHD+
 
7

Length

Max length4
Median length3
Mean length2.7373608
Min length2

Characters and Unicode

Total characters6389
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFHD
2nd rowFHD
3rd rowFHD
4th rowFHD
5th rowFHD

Common Values

ValueCountFrequency (%)
FHD 1253
53.7%
HD 620
26.6%
QHD 395
 
16.9%
HD+ 57
 
2.4%
FHD+ 7
 
0.3%
UHD 2
 
0.1%

Length

2024-02-14T07:37:34.779960image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-14T07:37:34.915032image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
fhd 1260
54.0%
hd 677
29.0%
qhd 395
 
16.9%
uhd 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
H 2334
36.5%
D 2334
36.5%
F 1260
19.7%
Q 395
 
6.2%
+ 64
 
1.0%
U 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6325
99.0%
Math Symbol 64
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H 2334
36.9%
D 2334
36.9%
F 1260
19.9%
Q 395
 
6.2%
U 2
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+ 64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6325
99.0%
Common 64
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
H 2334
36.9%
D 2334
36.9%
F 1260
19.9%
Q 395
 
6.2%
U 2
 
< 0.1%
Common
ValueCountFrequency (%)
+ 64
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6389
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H 2334
36.5%
D 2334
36.5%
F 1260
19.7%
Q 395
 
6.2%
+ 64
 
1.0%
U 2
 
< 0.1%

refresh_rate
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
120
852 
60
838 
90
459 
144
143 
165
 
42

Length

Max length3
Median length2
Mean length2.4443016
Min length2

Characters and Unicode

Total characters5705
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row120
2nd row120
3rd row120
4th row90
5th row90

Common Values

ValueCountFrequency (%)
120 852
36.5%
60 838
35.9%
90 459
19.7%
144 143
 
6.1%
165 42
 
1.8%

Length

2024-02-14T07:37:35.062783image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-14T07:37:35.184629image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
120 852
36.5%
60 838
35.9%
90 459
19.7%
144 143
 
6.1%
165 42
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 2149
37.7%
1 1037
18.2%
6 880
15.4%
2 852
 
14.9%
9 459
 
8.0%
4 286
 
5.0%
5 42
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5705
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2149
37.7%
1 1037
18.2%
6 880
15.4%
2 852
 
14.9%
9 459
 
8.0%
4 286
 
5.0%
5 42
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 5705
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2149
37.7%
1 1037
18.2%
6 880
15.4%
2 852
 
14.9%
9 459
 
8.0%
4 286
 
5.0%
5 42
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5705
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2149
37.7%
1 1037
18.2%
6 880
15.4%
2 852
 
14.9%
9 459
 
8.0%
4 286
 
5.0%
5 42
 
0.7%

os
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
android
2154 
other
 
114
ios
 
66

Length

Max length7
Median length7
Mean length6.7892031
Min length3

Characters and Unicode

Total characters15846
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowother
2nd rowandroid
3rd rowandroid
4th rowandroid
5th rowandroid

Common Values

ValueCountFrequency (%)
android 2154
92.3%
other 114
 
4.9%
ios 66
 
2.8%

Length

2024-02-14T07:37:35.481577image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-14T07:37:35.604228image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
android 2154
92.3%
other 114
 
4.9%
ios 66
 
2.8%

Most occurring characters

ValueCountFrequency (%)
d 4308
27.2%
o 2334
14.7%
r 2268
14.3%
i 2220
14.0%
a 2154
13.6%
n 2154
13.6%
t 114
 
0.7%
h 114
 
0.7%
e 114
 
0.7%
s 66
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15846
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 4308
27.2%
o 2334
14.7%
r 2268
14.3%
i 2220
14.0%
a 2154
13.6%
n 2154
13.6%
t 114
 
0.7%
h 114
 
0.7%
e 114
 
0.7%
s 66
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 15846
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 4308
27.2%
o 2334
14.7%
r 2268
14.3%
i 2220
14.0%
a 2154
13.6%
n 2154
13.6%
t 114
 
0.7%
h 114
 
0.7%
e 114
 
0.7%
s 66
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15846
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 4308
27.2%
o 2334
14.7%
r 2268
14.3%
i 2220
14.0%
a 2154
13.6%
n 2154
13.6%
t 114
 
0.7%
h 114
 
0.7%
e 114
 
0.7%
s 66
 
0.4%

extended_memory_available
Categorical

HIGH CORRELATION  UNIFORM 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
0
1167 
1
1167 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2334
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 1167
50.0%
1 1167
50.0%

Length

2024-02-14T07:37:35.723379image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-14T07:37:35.832064image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0 1167
50.0%
1 1167
50.0%

Most occurring characters

ValueCountFrequency (%)
0 1167
50.0%
1 1167
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2334
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1167
50.0%
1 1167
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2334
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1167
50.0%
1 1167
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2334
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1167
50.0%
1 1167
50.0%

extended_upto
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean377.54927
Minimum0
Maximum2048
Zeros1167
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2024-02-14T07:37:35.937189image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median16
Q31024
95-th percentile1024
Maximum2048
Range2048
Interquartile range (IQR)1024

Descriptive statistics

Standard deviation494.88869
Coefficient of variation (CV)1.3107923
Kurtosis1.0005694
Mean377.54927
Median Absolute Deviation (MAD)16
Skewness1.2373246
Sum881200
Variance244914.82
MonotonicityNot monotonic
2024-02-14T07:37:36.059383image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 1167
50.0%
1024 565
24.2%
256 257
 
11.0%
512 225
 
9.6%
2048 56
 
2.4%
128 44
 
1.9%
64 10
 
0.4%
32 9
 
0.4%
400 1
 
< 0.1%
ValueCountFrequency (%)
0 1167
50.0%
32 9
 
0.4%
64 10
 
0.4%
128 44
 
1.9%
256 257
 
11.0%
400 1
 
< 0.1%
512 225
 
9.6%
1024 565
24.2%
2048 56
 
2.4%
ValueCountFrequency (%)
2048 56
 
2.4%
1024 565
24.2%
512 225
 
9.6%
400 1
 
< 0.1%
256 257
 
11.0%
128 44
 
1.9%
64 10
 
0.4%
32 9
 
0.4%
0 1167
50.0%

primary_camera_rear
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.582991
Minimum2
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2024-02-14T07:37:36.214818image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile12
Q120
median50
Q364
95-th percentile108
Maximum200
Range198
Interquartile range (IQR)44

Descriptive statistics

Standard deviation36.178061
Coefficient of variation (CV)0.70135641
Kurtosis5.803257
Mean51.582991
Median Absolute Deviation (MAD)14
Skewness1.9362457
Sum120394.7
Variance1308.8521
MonotonicityNot monotonic
2024-02-14T07:37:36.412153image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
50 851
36.5%
64 378
16.2%
13 332
 
14.2%
48 229
 
9.8%
108 182
 
7.8%
12 94
 
4.0%
8 78
 
3.3%
200 64
 
2.7%
16 52
 
2.2%
5 16
 
0.7%
Other values (14) 58
 
2.5%
ValueCountFrequency (%)
2 2
 
0.1%
5 16
 
0.7%
8 78
 
3.3%
12 94
 
4.0%
12.2 4
 
0.2%
13 332
14.2%
16 52
 
2.2%
19 1
 
< 0.1%
20 7
 
0.3%
21 6
 
0.3%
ValueCountFrequency (%)
200 64
 
2.7%
180 1
 
< 0.1%
160 3
 
0.1%
108 182
 
7.8%
100 13
 
0.6%
64 378
16.2%
54 9
 
0.4%
50.3 3
 
0.1%
50 851
36.5%
48 229
 
9.8%

primary_camera_front
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)1.1%
Missing6
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean16.718814
Minimum0.3
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2024-02-14T07:37:36.559038image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile5
Q18
median16
Q316
95-th percentile32
Maximum60
Range59.7
Interquartile range (IQR)8

Descriptive statistics

Standard deviation11.59386
Coefficient of variation (CV)0.69346186
Kurtosis1.8503377
Mean16.718814
Median Absolute Deviation (MAD)8
Skewness1.4157557
Sum38921.4
Variance134.41759
MonotonicityNot monotonic
2024-02-14T07:37:36.699776image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
16 629
26.9%
8 542
23.2%
32 415
17.8%
5 283
12.1%
13 116
 
5.0%
12 94
 
4.0%
20 50
 
2.1%
50 50
 
2.1%
10 46
 
2.0%
60 28
 
1.2%
Other values (15) 75
 
3.2%
ValueCountFrequency (%)
0.3 2
 
0.1%
1 1
 
< 0.1%
1.2 1
 
< 0.1%
2 9
 
0.4%
5 283
12.1%
7 12
 
0.5%
8 542
23.2%
9.5 1
 
< 0.1%
10 46
 
2.0%
10.1 1
 
< 0.1%
ValueCountFrequency (%)
60 28
 
1.2%
50 50
 
2.1%
48 3
 
0.1%
44 13
 
0.6%
40 6
 
0.3%
32 415
17.8%
25 6
 
0.3%
24 8
 
0.3%
20 50
 
2.1%
16 629
26.9%

Interactions

2024-02-14T07:37:27.129812image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:09.926492image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:11.523242image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:13.155667image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:14.743111image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:16.276187image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:17.825337image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:19.326813image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:21.020216image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:22.557013image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:24.034518image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:25.514916image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:27.263513image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:10.071388image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:11.655665image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:13.299510image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:14.880708image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:16.457578image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:17.955976image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:19.456999image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:21.150778image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:22.680539image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:24.163002image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:25.650862image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:27.388081image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:10.196707image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:11.779159image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:13.439044image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:15.002284image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:16.588141image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:18.080503image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:19.603502image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:21.268694image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:22.810764image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:24.312541image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:25.783427image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:27.518651image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:10.361492image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:11.902291image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:13.573837image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:15.133845image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:16.716112image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:18.214039image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:19.748027image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:21.393683image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:22.938227image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:24.461065image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:25.933935image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:27.647212image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:10.490905image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:12.018741image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:13.702408image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:15.252458image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:16.838718image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:18.361543image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:19.870629image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:21.532254image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:23.059873image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:24.578094image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:26.066426image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:27.769262image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:10.618478image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:12.137764image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:13.820801image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:15.380021image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:16.960271image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:18.493032image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:19.992690image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:21.644844image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:23.173134image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:24.697873image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:26.196875image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:27.892940image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:10.737149image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:12.273369image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:13.945492image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:15.510581image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:17.081865image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:18.613560image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:20.116276image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:21.777436image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:23.296596image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:24.810505image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:26.350274image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:28.022353image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:10.863603image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:12.416829image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:14.072108image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:15.635185image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:17.197465image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:18.733985image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:20.247842image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:21.901984image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:23.412388image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:24.925673image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:26.488810image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:28.157889image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:10.986469image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:12.537187image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:14.189674image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:15.769842image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:17.319935image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:18.846087image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:20.394378image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:22.022578image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:23.538291image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:25.040076image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:26.620176image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:28.296425image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:11.113127image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:12.671460image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:14.337187image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:15.884294image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:17.439218image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:18.960690image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:20.510963image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:22.146208image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:23.649271image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:25.153769image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:26.742333image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:28.438949image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:11.238642image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:12.782149image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:14.472729image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:16.002604image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:17.571765image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:19.075958image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:20.626182image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:22.276753image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:23.760433image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:25.261640image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:26.863963image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:28.570071image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:11.389690image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:13.033434image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:14.613542image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:16.131727image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:17.702780image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:19.205026image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:20.755962image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:22.432208image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:23.909979image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:25.393200image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-14T07:37:27.001504image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Correlations

2024-02-14T07:37:36.840369image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
battery_capacitybrand_nameextended_memory_availableextended_uptofast_chargingfast_charging_availablehas_5ghas_ir_blasterhas_nfcinternal_memorynum_coresnum_front_camerasnum_rear_camerasospriceprimary_camera_frontprimary_camera_rearprocessor_brandprocessor_speedram_capacityratingrefresh_rateresolutionscreen_size
battery_capacity1.0000.2220.2770.224-0.1290.3520.3370.1390.3270.0320.2100.1360.3120.288-0.189-0.0770.1800.230-0.0730.009-0.1760.2050.1620.332
brand_name0.2221.0000.4520.152-0.0770.3740.4110.6850.478-0.0380.2160.1200.2780.817-0.0980.0710.1140.503-0.056-0.0270.0010.2970.312-0.031
extended_memory_available0.2770.4521.0000.935-0.5270.1560.4560.0800.502-0.451-0.0180.1230.2110.212-0.581-0.367-0.1940.437-0.562-0.478-0.4300.4480.393-0.260
extended_upto0.2240.1520.9351.000-0.4820.2560.4880.1590.470-0.3370.0650.0760.1540.152-0.496-0.301-0.0770.269-0.467-0.380-0.3270.2510.202-0.161
fast_charging-0.129-0.077-0.527-0.4821.0001.0000.4690.3550.4560.5720.0900.0820.1970.1250.6670.6310.4990.1470.6690.6840.6450.3540.3100.464
fast_charging_available0.3520.3740.1560.2561.0001.0000.4010.1820.2850.4910.3680.0790.4850.0780.4510.4360.5110.3980.4170.5030.4820.4880.5230.366
has_5g0.3370.4110.4560.4880.4690.4011.0000.1760.4880.6040.1760.0790.3630.0600.6850.4650.4390.7300.6460.6290.6480.6430.5220.370
has_ir_blaster0.1390.6850.0800.1590.3550.1820.1761.0000.1180.2000.1120.0100.2150.0800.1680.1690.2390.2530.2230.2090.2070.2600.3430.218
has_nfc0.3270.4780.5020.4700.4560.2850.4880.1181.0000.5310.0590.1470.3060.2880.7150.4380.2670.4880.6340.5540.5870.5280.5330.316
internal_memory0.032-0.038-0.451-0.3370.5720.4910.6040.2000.5311.0000.2310.1330.2470.1870.7360.5590.5070.2770.6410.8360.7380.3320.3280.528
num_cores0.2100.216-0.0180.0650.0900.3680.1760.1120.0590.2311.0000.0590.2450.6300.1420.2560.3540.5430.1690.3000.1390.1590.1450.231
num_front_cameras0.1360.1200.1230.0760.0820.0790.0790.0100.1470.1330.0591.0000.0800.0770.2190.1530.0800.1330.1750.1950.1280.0780.1430.129
num_rear_cameras0.3120.2780.2110.1540.1970.4850.3630.2150.3060.2470.2450.0801.0000.0990.3820.4870.4200.2560.3020.3640.4080.2640.2820.222
os0.2880.8170.2120.1520.1250.0780.0600.0800.2880.1870.6300.0770.0991.0000.3510.019-0.1140.7360.2620.1190.1210.1260.1590.099
price-0.189-0.098-0.581-0.4960.6670.4510.6850.1680.7150.7360.1420.2190.3820.3511.0000.6220.4440.2270.8160.7800.8010.3170.3130.410
primary_camera_front-0.0770.071-0.367-0.3010.6310.4360.4650.1690.4380.5590.2560.1530.4870.0190.6221.0000.6060.2050.5460.6520.7000.3200.3110.348
primary_camera_rear0.1800.114-0.194-0.0770.4990.5110.4390.2390.2670.5070.3540.0800.420-0.1140.4440.6061.0000.1620.3970.5840.5790.3300.2920.461
processor_brand0.2300.5030.4370.2690.1470.3980.7300.2530.4880.2770.5430.1330.2560.7360.2270.2050.1621.000-0.0830.0110.0020.2720.2790.047
processor_speed-0.073-0.056-0.562-0.4670.6690.4170.6460.2230.6340.6410.1690.1750.3020.2620.8160.5460.397-0.0831.0000.6880.7230.3570.3190.418
ram_capacity0.009-0.027-0.478-0.3800.6840.5030.6290.2090.5540.8360.3000.1950.3640.1190.7800.6520.5840.0110.6881.0000.8220.3900.3540.542
rating-0.1760.001-0.430-0.3270.6450.4820.6480.2070.5870.7380.1390.1280.4080.1210.8010.7000.5790.0020.7230.8221.0000.3690.3440.381
refresh_rate0.2050.2970.4480.2510.3540.4880.6430.2600.5280.3320.1590.0780.2640.1260.3170.3200.3300.2720.3570.3900.3691.0000.3010.543
resolution0.1620.3120.3930.2020.3100.5230.5220.3430.5330.3280.1450.1430.2820.1590.3130.3110.2920.2790.3190.3540.3440.3011.0000.090
screen_size0.332-0.031-0.260-0.1610.4640.3660.3700.2180.3160.5280.2310.1290.2220.0990.4100.3480.4610.0470.4180.5420.3810.5430.0901.000

Missing values

2024-02-14T07:37:28.918557image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-14T07:37:29.296709image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-02-14T07:37:29.573987image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

brand_namemodelpriceratinghas_5ghas_nfchas_ir_blasterprocessor_brandnum_coresprocessor_speedram_capacityinternal_memorybattery_capacityfast_charging_availablefast_chargingnum_rear_camerasnum_front_camerasscreen_sizeresolutionrefresh_rateosextended_memory_availableextended_uptoprimary_camera_rearprimary_camera_front
0oneplusOnePlus V Flip7999089.0TrueTrueFalsedimensity8.03.2012.0256.04500.0167.0216.78FHD120other00.050.032.0
1tecnoTecno Camon 20 Pro 4G1699082.0FalseTrueFalsehelio8.02.208.0256.05000.0133.0316.67FHD120android00.064.032.0
2xiaomiXiaomi Redmi Note 12 Pro (8GB RAM + 256GB)2379982.0TrueFalseTruedimensity8.02.608.0256.05000.0167.0316.67FHD120android00.050.016.0
3realmeRealme 8 5G1699975.0TrueFalseFalsedimensity8.02.204.0128.05000.0118.0316.50FHD90android11024.048.016.0
4xiaomiXiaomi Redmi Note 10 JE 5G1049074.0TrueFalseTruesnapdragon8.02.004.064.04800.0118.0316.50FHD90android11024.048.08.0
5lenovoLenovo Legion Pro 25999989.0TrueTrueFalsesnapdragon8.02.8416.0512.05000.01120.0216.50FHD144android00.064.020.0
6samsungSamsung Galaxy M52 5G (8GB RAM + 128GB)2399086.0TrueTrueFalsesnapdragon8.02.408.0128.05000.0125.0316.70FHD120android11024.064.032.0
7micromaxMicromax IN 2C579060.0FalseFalseFalsetiger8.01.803.032.05000.00NaN116.52HD60android1256.08.05.0
8sonySony Xperia 10 V3999984.0TrueTrueFalsesnapdragon8.02.206.0128.05000.0121.0316.10FHD60android00.048.08.0
9xiaomiXiaomi Redmi 10 Prime (6GB RAM + 128GB)1345877.0FalseFalseTruehelio8.02.006.0128.06000.0118.0416.50FHD90android1512.050.08.0
brand_namemodelpriceratinghas_5ghas_nfchas_ir_blasterprocessor_brandnum_coresprocessor_speedram_capacityinternal_memorybattery_capacityfast_charging_availablefast_chargingnum_rear_camerasnum_front_camerasscreen_sizeresolutionrefresh_rateosextended_memory_availableextended_uptoprimary_camera_rearprimary_camera_front
2324nokiaNokia G310 5G1699974.0TrueFalseFalsesnapdragon8.02.204.0128.05000.0120.0316.56HD90android11024.050.08.0
2325xiaomiXiaomi Redmi Note 12 Explorer2499989.0TrueTrueTruedimensity8.02.608.0256.04300.01120.0316.67FHD120android1512.0200.016.0
2326realmeRealme GT Neo 3 5G (8GB RAM + 256GB)2470083.0TrueTrueFalsedimensity8.02.858.0256.05000.0180.0316.70FHD120android00.050.016.0
2327vivoVivo Y75T2199079.0TrueFalseFalsedimensity8.02.608.0128.06000.0133.0216.67FHD120android00.050.016.0
2328motorolaMotorola Moto G6 (4GB RAM + 64GB)979067.0FalseFalseFalsesnapdragon8.01.804.064.03000.01NaN215.70FHD60android1256.012.016.0
2329realmeRealme GT 2 Fold 5G9499088.0TrueTrueFalsesnapdragon8.03.208.0128.05000.0165.0218.00FHD120android00.050.016.0
2330samsungSamsung Galaxy F34 5G1599983.0TrueTrueFalseexynos8.02.406.0128.06000.0125.0316.50FHD120android11024.050.013.0
2331vivoVivo Y33t 4G 2023849971.0FalseFalseFalsehelio8.02.006.0128.05000.0115.0216.56HD60android11024.013.08.0
2332xiaomiXiaomi 11 LE 5G2499083.0TrueTrueTruesnapdragon8.02.406.0128.04250.0133.0316.55FHD90android00.064.020.0
2333motorolaMotorola Moto E351199970.0FalseFalseFalseunisoc8.02.004.064.05000.0118.0316.50HD90android11024.016.08.0